Methods to train message passing neural network models on polymer structures.
Project description
PolyIDTM provides a framework for building, training, and predicting polymer properities using graph neural networks. The codes leverages nfp, for building tensorflow-based message-passing neural networ, and m2p, for building polymer structures. To understand and use the pipeline, the following examples have been provided:
- Building polymer structures:
examples/example_generate_polymer_structures.ipynb
- Checking domain of validity:
examples/example_determine_domain-of-validity.ipynb
- Training a graph neural network:
examples/example_generate_and_train_models.ipynb
- Predicting with the trained model:
examples/example_predict_with_trained_models.ipynb
Details for the methods are forthcoming in an upcoming manuscript.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
polyid-0.1.1.2.tar.gz
(38.0 kB
view hashes)
Built Distribution
polyid-0.1.1.2-py3-none-any.whl
(22.6 kB
view hashes)